import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K

# Load Dataset
# (x_train, _), (x_test, _) = mnist.load_data()
f = np.load('mnist.npz')
x_train, y_train = f['x_train'], f['y_train']
x_test, y_test = f['x_test'], f['y_test']

# Scale Dataset values to lie between 0 and 1
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
# Add Noise to our MNNIST Dataset by sampling random values from Gaussian distribution by using np.random.normal() and adding it to our original images to change pixel values
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
# Visualising the Noisy Digits using Matplotlib
# n = 10 # change this number to visualise more digits.
# plt.figure(figsize=(20, 2))
# for i in range(1,n):
#     ax = plt.subplot(1, n, i)
#     plt.imshow(x_test_noisy[i].reshape(28, 28))
#     plt.gray()
# ax.get_xaxis().set_visible(False)
# ax.get_yaxis().set_visible(False)
# plt.show()

# Specify the Input Layer size which is 28x28x1
input_img = Input(shape=(28, 28, 1))
# Model Construction
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
# At this point the representation is (7, 7, 32)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='relu', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

autoencoder.fit(x_train_noisy, x_train,epochs=100,batch_size=128,shuffle=True,validation_data=(x_test_noisy, x_test))

reconstruct_img = autoencoder.predict(x_test_noisy)

n = 10 # change this number to visualise more digits.
plt.figure(figsize=(20, 2))
for i in range(1, n):
    ax = plt.subplot(1, n, i)
    plt.imshow(reconstruct_img[i].reshape(28, 28))
    plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()